{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T16:32:40Z","timestamp":1781886760501,"version":"3.54.5"},"reference-count":71,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,8,23]],"date-time":"2017-08-23T00:00:00Z","timestamp":1503446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Support Fund - Jordan","award":["ENG\/1\/9\/2015"],"award-info":[{"award-number":["ENG\/1\/9\/2015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as     88 . 8 %     and     90 . 2 %    , respectively, for the subject-dependent training procedure, and     80 . 8 %     and     87 . 8 %    , respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations.<\/jats:p>","DOI":"10.3390\/s17091937","type":"journal-article","created":{"date-parts":[[2017,8,23]],"date-time":"2017-08-23T11:32:27Z","timestamp":1503487947000},"page":"1937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1296-0231","authenticated-orcid":false,"given":"Rami","family":"Alazrai","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hisham","family":"Alwanni","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Freiburg, Freiburg 79098, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yara","family":"Baslan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Applied Medical Sciences, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasim","family":"Alnuman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Applied Medical Sciences, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9399-5769","authenticated-orcid":false,"given":"Mohammad","family":"Daoud","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain\u2013computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin. Neurophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1111\/j.1469-8986.2006.00456.x","article-title":"Breaking the silence: Brain\u2013computer interfaces (BCI) for communication and motor control","volume":"43","author":"Birbaumer","year":"2006","journal-title":"Psychophysiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.mayocp.2011.12.008","article-title":"Brain-computer interfaces in medicine","volume":"87","author":"Shih","year":"2012","journal-title":"Mayo Clin. Proc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sitaram, R., Caria, A., Veit, R., Gaber, T., Rota, G., Kuebler, A., and Birbaumer, N. (2007). FMRI brain-computer interface: A tool for neuroscientific research and treatment. Comput. Intell. Neurosci., 2007.","DOI":"10.1155\/2007\/25487"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s11517-008-0345-8","article-title":"Delta band contribution in cue based single trial classification of real and imaginary wrist movements","volume":"46","author":"Vuckovic","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liao, K., Xiao, R., Gonzalez, J., and Ding, L. (2014). Decoding individual finger movements from one hand using human EEG signals. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085192"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ge, S., Wang, R., and Yu, D. (2014). Classification of four-class motor imagery employing single-channel electroencephalography. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0098019"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yong, X., and Menon, C. (2015). EEG classification of different imaginary movements within the same limb. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0121896"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TBME.2015.2467312","article-title":"EEG source imaging enhances the decoding of complex right-hand motor imagery tasks","volume":"63","author":"Edelman","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/BF00227183","article-title":"Localization of grasp representations in humans by positron emission tomography","volume":"112","author":"Grafton","year":"1996","journal-title":"Exp. Brain Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TNSRE.2003.814454","article-title":"Graz-BCI: State of the art and clinical applications","volume":"11","author":"Pfurtscheller","year":"2003","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1088\/1741-2560\/1\/3\/002","article-title":"Motor imagery classification by means of source analysis for brain\u2013computer interface applications","volume":"1","author":"Qin","year":"2004","journal-title":"J. Neural Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.3390\/s120201211","article-title":"Brain computer interfaces, a review","volume":"12","year":"2012","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/ACCESS.2016.2637409","article-title":"A brain-computer interface based on a few-channel EEG-fNIRS bimodal system","volume":"5","author":"Ge","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.neulet.2012.11.039","article-title":"Action observation versus motor imagery in learning a complex motor task: A short review of literature and a kinematics study","volume":"540","author":"Gatti","year":"2013","journal-title":"Neurosci. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1109\/TBME.2007.897815","article-title":"Control of an electrical prosthesis with an SSVEP-based BCI","volume":"55","author":"Pfurtscheller","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"449","DOI":"10.3109\/17482961003777470","article-title":"A brain-computer interface for long-term independent home use","volume":"11","author":"Sellers","year":"2010","journal-title":"Amyotroph. Lateral Scler."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/86.847808","article-title":"The mental prosthesis: Assessing the speed of a P300-based brain-computer interface","volume":"8","author":"Donchin","year":"2000","journal-title":"IEEE Trans. Rehabil. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/S0304-3940(00)01471-3","article-title":"Brain oscillations control hand orthosis in a tetraplegic","volume":"292","author":"Pfurtscheller","year":"2000","journal-title":"Neurosci. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tang, Z., Sun, S., Zhang, S., Chen, Y., Li, C., and Chen, S. (2016). A brain-machine interface based on ERD\/ERS for an upper-limb exoskeleton control. Sensors, 16.","DOI":"10.3390\/s16122050"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/TBME.2004.827062","article-title":"An asynchronously controlled EEG-based virtual keyboard: Improvement of the spelling rate","volume":"51","author":"Scherer","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/0028-3932(95)00073-C","article-title":"Mental imagery in the motor context","volume":"33","author":"Jeannerod","year":"1995","journal-title":"Neuropsychologia"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/5.939829","article-title":"Motor imagery and direct brain-computer communication","volume":"89","author":"Pfurtscheller","year":"2001","journal-title":"Proc. IEEE"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"17849","DOI":"10.1073\/pnas.0403504101","article-title":"Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans","volume":"101","author":"Wolpaw","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/TNSRE.2010.2077654","article-title":"EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies","volume":"18","author":"Royer","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Doud, A.J., Lucas, J.P., Pisansky, M.T., and He, B. (2011). Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0026322"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"046003","DOI":"10.1088\/1741-2560\/10\/4\/046003","article-title":"Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain\u2013computer interface","volume":"10","author":"LaFleur","year":"2013","journal-title":"J. Neural Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/S0013-4694(97)00147-8","article-title":"Volume conduction effects in EEG and MEG","volume":"106","author":"Reinders","year":"1998","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1016\/j.amc.2006.07.005","article-title":"Time-frequency analysis of the first and the second heartbeat sounds","volume":"184","author":"Debbal","year":"2007","journal-title":"Appl. Math. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/MSP.2013.2265914","article-title":"Time-frequency processing of nonstationary signals: Advanced TFD design to aid diagnosis with highlights from medical applications","volume":"30","author":"Boashash","year":"2013","journal-title":"IEEE Signal Process Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.knosys.2016.05.027","article-title":"Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study","volume":"106","author":"Boashash","year":"2016","journal-title":"Knowledge-Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Veluvolu, K.C. (2017). Time-frequency analysis of non-stationary biological signals with sparse linear regression based fourier linear combiner. Sensors, 17.","DOI":"10.3390\/s17061386"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3316","DOI":"10.1016\/j.neuroimage.2011.11.053","article-title":"Single trial discrimination of individual finger movements on one hand: A combined MEG and EEG study","volume":"59","author":"Quandt","year":"2012","journal-title":"NeuroImage"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1016\/j.ins.2007.11.012","article-title":"Classifying mental tasks based on features of higher-order statistics from EEG signals in brain\u2013computer interface","volume":"178","author":"Zhou","year":"2008","journal-title":"Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"140053","DOI":"10.1038\/sdata.2014.53","article-title":"Electromyography data for non-invasive naturally-controlled robotic hand prostheses","volume":"1","author":"Atzori","year":"2014","journal-title":"Sci. Data"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, B., Wu, X., Lv, Z., Zhang, L., and Guo, X. (2016). A fully automated trial selection method for optimization of motor imagery based brain-computer interface. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0162657"},{"key":"ref_37","unstructured":"Lan, T., Erdogmus, D., Adami, A., Pavel, M., and Mathan, S. (2005, January 1\u20134). Salient EEG channel selection in brain computer interfaces by mutual information maximization. Proceedings of the IEEE 27th Annual International Conference of the Engineering in Medicine and Biology Society, Shanghai, China."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","article-title":"EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis","volume":"134","author":"Delorme","year":"2004","journal-title":"J. Neurosci. Methods"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Herrero, G., De Clercq, W., Anwar, H., Kara, O., Egiazarian, K., Van Huffel, S., and Van Paesschen, W. (2006, January 7\u20139). Automatic removal of ocular artifacts in the EEG without an EOG reference channel. Proceedings of the IEEE 7th Nordic Signal Processing Symposium, Rejkjavik, Iceland.","DOI":"10.1109\/NORSIG.2006.275210"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Castiglioni, P. (2005). Choi\u2013williams distribution. Encyclopedia of Biostatistics, John Wiley & Sons, Ltd.","DOI":"10.1002\/0470011815.b2a12012"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","article-title":"Epileptic seizure detection in EEGs using time\u2013frequency analysis","volume":"13","author":"Tzallas","year":"2009","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Boubchir, L., Al-Maadeed, S., and Bouridane, A. (2014, January 4\u20139). On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854733"},{"key":"ref_43","unstructured":"Boashash, B. (2015). Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Academic Press."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1109\/5.30749","article-title":"Time-frequency distributions-a review","volume":"77","author":"Cohen","year":"1989","journal-title":"Proc. IEEE"},{"key":"ref_45","unstructured":"Cohen, L. (1995). Time-Frequency Analysis, Prentice Hall PTR."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s11517-010-0590-5","article-title":"New feature extraction approach for epileptic EEG signal detection using time-frequency distributions","volume":"48","author":"Trigueros","year":"2010","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_47","unstructured":"Hahn, S.L. (1996). Hilbert Transforms in Signal Processing, Artech House."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.patcog.2014.08.016","article-title":"Principles of time\u2013frequency feature extraction for change detection in non-stationary signals: Applications to newborn {EEG} abnormality detection","volume":"48","author":"Boashash","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_49","first-page":"372","article-title":"Time-frequency signal analysis","volume":"35","author":"Claasen","year":"1980","journal-title":"Philips J. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/ASSP.1989.28057","article-title":"Improved time-frequency representation of multicomponent signals using exponential kernels","volume":"37","author":"Choi","year":"1989","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_51","unstructured":"Swami, A., Mendel, J., and Nikias, C. (2000). Higher-order spectra analysis (hosa) toolbox. Version, 2, Available online: https:\/\/labcit.ligo.caltech.edu\/ rana\/mat\/HOSA\/."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"016007","DOI":"10.1088\/1741-2560\/7\/1\/016007","article-title":"Automatic classification of background EEG activity in healthy and sick neonates","volume":"7","author":"Thordstein","year":"2010","journal-title":"J. Neural Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1016\/j.clinph.2008.02.001","article-title":"A comparison of quantitative EEG features for neonatal seizure detection","volume":"119","author":"Greene","year":"2008","journal-title":"Clin. Neurophysiol."},{"key":"ref_54","first-page":"218","article-title":"A multi-stage system for the automated detection of epileptic seizures in neonatal EEG","volume":"26","author":"Mitra","year":"2009","journal-title":"J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Hassan, A.R., Bashar, S.K., and Bhuiyan, M.I.H. (2015, January 10\u201313). On the classification of sleep states by means of statistical and spectral features from single channel electroencephalogram. Proceedings of the IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, India.","DOI":"10.1109\/ICACCI.2015.7275950"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.knosys.2015.08.004","article-title":"Application of entropies for automated diagnosis of epilepsy using EEG signals: A review","volume":"88","author":"Acharya","year":"2015","journal-title":"Knowledge-Based Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.knosys.2016.01.040","article-title":"Automated detection and localization of myocardial infarction using electrocardiogram: A comparative study of different leads","volume":"99","author":"Acharya","year":"2016","journal-title":"Knowledge-Based Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/S0165-1684(00)00236-X","article-title":"A measure of some time\u2013frequency distributions concentration","volume":"81","year":"2001","journal-title":"Signal Process."},{"key":"ref_59","unstructured":"Doyle, S., Feldman, M., Tomaszewski, J., Shih, N., and Madabhushi, A. (April, January 30). Cascaded multi-class pairwise classifier (CASCAMPA) for normal, cancerous, and cancer confounder classes in prostate histology. Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA."},{"key":"ref_60","unstructured":"Granitto, P.M., R\u00e9bola, A., Cervi\u00f1o, U., Gasperi, F., Biasoli, F., and Ceccatto, H.A. (2005, January 29\u201330). Cascade classifiers for multiclass problems. Proceedings of the 7-th Argentine Symposium on Artificial Intelligence (ASAI), Rosario, Argentina."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_63","first-page":"841","article-title":"On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes","volume":"2","author":"Ng","year":"2002","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.patrec.2009.09.019","article-title":"Recognition of human activities using SVM multi-class classifier","volume":"31","author":"Qian","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kre\u00dfel, U.H.G. (1999). Pairwise classification and support vector machines. Advances in Kernel Methods, MIT Press.","DOI":"10.7551\/mitpress\/1130.003.0020"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","article-title":"A comparison of methods for multiclass support vector machines","volume":"13","author":"Hsu","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1109\/JBHI.2012.2237409","article-title":"Detection of seizure and epilepsy using higher order statistics in the EMD domain","volume":"17","author":"Alam","year":"2013","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Alazrai, R., Momani, M., and Daoud, M.I. (2017). Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation. Appl. Sci., 7.","DOI":"10.3390\/app7040316"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1109\/TNSRE.2017.2655542","article-title":"Optimized motor imagery paradigm based on imagining Chinese characters writing movement","volume":"25","author":"Qiu","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"3432","DOI":"10.1523\/JNEUROSCI.6107-09.2010","article-title":"Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals","volume":"30","author":"Bradberry","year":"2010","journal-title":"J. Neurosci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1109\/JPROC.2012.2185009","article-title":"Evolving signal processing for brain\u2013computer interfaces","volume":"100","author":"Makeig","year":"2012","journal-title":"Proc. IEEE"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/1937\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:43:05Z","timestamp":1760208185000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/9\/1937"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,23]]},"references-count":71,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["s17091937"],"URL":"https:\/\/doi.org\/10.3390\/s17091937","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,23]]}}}